GPUscout Bottleneck Analysis

• GPUscout Bottleneck Analysis is a distributed methodology that diagnoses GPU kernel performance variability using sharded trace analysis and CUPTI/NCU metrics.
• It computes detailed performance metrics like occupancy, memory/compute throughput, and roofline fractions to classify bottlenecks accurately.
• Integration with MT4G enables automated GPU topology discovery, refining hardware parameters for high-fidelity roofline modeling.

GPUscout Bottleneck Analysis is a distributed methodology for diagnosing GPU kernel performance variability and identifying bottlenecks using large-scale Nsight Compute traces. The analysis operates by sharding profiler outputs into discrete time intervals, mapping relevant CUPTI or NCU counters, and applying a workflow that computes per-kernel resource utilizations and bottleneck classifications. Recent advances leverage automated hardware topology discovery, such as via the MT4G microbenchmark suite, to achieve high-fidelity roofline modeling and accurate bottleneck diagnostics in environments characterized by rapid hardware evolution and complex heterogeneous GPU deployments (Lahiry et al., 17 Jun 2025, Vanecek et al., 8 Nov 2025).

1. Distributed Data Partitioning and Analysis Pipeline

Handling multi-terabyte trace dumps necessitates a scalable and memory-efficient analysis architecture. GPUscout employs a time-sharded distributed pipeline in which trace event timestamps are partitioned into $N$ equal-duration shards, with each MPI rank assigned one or more shards. The timestamp range $[T_{min},\, T_{max}]$ is split such that each event with timestamp $t_i$ is mapped to shard $j = \lfloor (t_i - T_{min})/\Delta \rfloor$, where $\Delta = (T_{max} - T_{min})/N$.

Shard-to-rank assignment can be blockwise, for homogenous trace density, or round-robin/cyclic if cardinality varies. If event distribution is known in advance, a one-dimensional bin-packing heuristic can further balance per-rank workloads: assign contiguous blocks so that $\sum_{j \in block_r} K_j \approx (\sum_j K_j)/P$, where $K_j$ is the count in shard $j$.

Data extraction occurs in two stages:

• Stage 1 (Extract & Shard): Each MPI rank opens local SQLite traces, runs time-ranged SQL queries, joins necessary CUPTI tables (e.g., ACTIVITY_KERNEL, ACTIVITY_MEMCPY, TARGET_INFO_GPU), and writes per-shard Parquet files.
• Stage 2 (Aggregate & Analyze): Ranks process only their generated Parquet shards, bin data into fixed-length intervals (e.g., 1 s), and via MPI_Allreduce or explicit round-robin exchange, aggregate partial summaries (global sums, means, stddev, min, max). IQR-based outlier detection is executed in parallel to flag anomalous bins or shards for further inspection.

This pipeline never requires whole-trace aggregation on any node, eliminating centralized bottlenecks and facilitating near-linear scaling with shard and node count (Lahiry et al., 17 Jun 2025).

2. Performance Metrics and Computational Formulas

GPUscout bottleneck analysis computes kernel- and interval-level metrics central to resource utilization and bottleneck identification. Key metrics include:

• Durations:
  • $T_{total} = t_{end} - t_{start}$
  • $$T_{mem} = \sum_k (t^{(k)}_{memcpy\, end} - t^{(k)}_{memcpy\, start})$$ for host-device transfers in a kernel.
  • $$T_{comp} = T_{total} - T_{mem} - T_{kernel\, idle\, stall}$$, optionally subtracting stall counters.
• Occupancy:
  • $$O_a = \text{ActiveWarps}/(\text{SM\_count} \times \text{maxWarpSlotsPerSM})$$
  • Theoretical occupancy $O_w$ from resource-limited minima.
• Memory Throughput:
  • $$\text{BytesTransferred} = (\text{gld\_transactions} \times 128\,\mathrm{B}) + (\text{gst\_transactions} \times 128\,\mathrm{B})$$
  • $$\text{BW}_{measured} = \text{BytesTransferred}/T_{mem}$$
  • $U_{BW} = \text{BW}_{measured}/\text{BW}_{peak}$
• Compute Throughput:
  • $$U_{comp} = \text{InstExecuted}/(\text{Inst}_{peak} \times T_{comp})$$
• Roofline Fractions:
  • $U_{mem\, frac} = T_{mem}/T_{total}$
  • $U_{comp\, frac} = T_{comp}/T_{total}$

These metrics, expressed per interval or kernel, are used for outlier and bottleneck analysis. All formulas directly reflect those in (Lahiry et al., 17 Jun 2025).

3. Variability Diagnosis and Bottleneck Classification

Statistical diagnosis proceeds by estimating mean $(\mu)$, standard deviation $(\sigma)$, and coefficient of variation $(CV)$ for key metrics (e.g., $U_{BW}$) across shards: $\mu_x = (1/N)\sum_j x_j$, $\sigma_x = \sqrt{(1/N)\sum_j (x_j - \mu_x)^2}$, $CV_x = \sigma_x/\mu_x$. Outliers are flagged when metrics fall outside $[Q_1 - 1.5\,\text{IQR}, Q_3 + 1.5\,\text{IQR}]$.

For anomalous shards, correlation analysis is performed on time-aligned hardware counters (e.g., $l2\_subp0\_read\_stall$, $global\_load\_latency$, $sm\_\_\_active\_warps.sum$), computing Pearson $r$ or Spearman $\rho$ with kernel runtimes. For instance, $|\rho(T_{total}, L_{mem})| > 0.7$ indicates memory-bound slowdowns, whereas strongly negative correlation with ActiveWarps indicates compute starvation.

Bottleneck classification uses threshold-based rules:

• If $U_{BW}^{(i)} > \alpha_{mem}$ (e.g., 0.6), classify as "memory-bound."
• Else if $U_{comp}^{(i)} > \alpha_{comp}$ (e.g., 0.6), classify as "compute-bound."
• Otherwise, classify as "I/O-bound/latency-bound."

Logistic regression or decision tree classifiers over $(U_{mem\, frac}, U_{comp\, frac}, O_a)$ may be employed to refine assignments (Lahiry et al., 17 Jun 2025).

4. Integration of Automated GPU Topology Discovery

The MT4G tool enhances bottleneck analysis by supplying high-fidelity, directly-measured hardware parameters traditionally unavailable or inaccurate in vendor documentation (Vanecek et al., 8 Nov 2025). MT4G runs an extensive suite of ≈50 microbenchmarks (e.g., pointer-chase latency, cache size and line size probes, bandwidth tests) employing statistical change-point detection (e.g., Kolmogorov–Smirnov test):

• Extracted attributes include cache sizes, latencies, bandwidths, line size, fetch granularity, bank counts, and physical sharing maps for all relevant memory elements (L1, texture, constant, L2, device DRAM for NVIDIA; vL1, sL1d, LDS, L2, L3, device DRAM for AMD).
• MT4G’s results are exported as JSON/CSV, distinguishing API-accessible from benchmarked values.

GPUscout’s initialization phase ingests MT4G profiles, updating its hardware models for cache sizes, DRAM bandwidths, and other ceilings. Kernel arithmetic intensity $AI$ is then evaluated as $AI = \text{FLOPS}/\text{bytes transferred}$; roofline and island-model boundaries are updated to reflect actual device measurements, not static datasheets.

Notably, the integration improves both automation and accuracy:

• Every new NVIDIA/AMD GPU may be profiled “out of the box.”
• Measured bandwidths account for dynamic configuration (e.g., MIG slices, driver settings).
• MT4G’s reported statistical confidence metrics (e.g., KS significance $\alpha$) are traceable within the bottleneck classifier.

Limitations include run time for full benchmark suite (6–14 minutes NVIDIA; ≈1 minute AMD; partial runs possible), incomplete benchmarks for emerging hardware elements (e.g., AMD CDNA3 L3), and corner-case discrepancies in known parameters. This approach closes the gap between raw counters and true hardware performance ceilings (Vanecek et al., 8 Nov 2025).

5. Case Study: Large-Scale Molecular Dynamics Bottleneck Analysis

In a detailed application, GPUscout analyzed a GROMACS-style molecular dynamics simulation on NVIDIA A100 hardware: 1,000 kernels per timestep over a 600 s profile, with ≈500 GB SQLite trace (Lahiry et al., 17 Jun 2025). The workflow:

• Trace was sharded into $N=600$ intervals (Δ=1 s) distributed over $P=10$ MPI ranks (60 shards per rank).
• Each rank generated ≈50 GB Parquet per 60 s of trace in Stage 1.
• All data was binned into 1 s intervals and aggregated to compute $\mu$ and $\sigma$ of $U_{BW}$ across bins ($\mu=0.35$, $\sigma=0.12$, $CV≈0.34$).
• Four anomalous bins (shards 120–123 s, $U_{BW}>0.7$) highlighted periods of high memory utilization.

A deep dive on bin 122 revealed:

Kernel	$T_{comp}$ ($\mu\pm\sigma$)	$T_{mem}$ ($\mu\pm\sigma$)	$BW_{meas}$	$U_{BW}$	$O_a$	Class
MD>force	120±10 μs	280±30 μs	400 GB/s	0.80	0.28	mem-bound
MD>pair	80±5 μs	30±8 μs	120 GB/s	0.24	0.45	comp-bound

Visualization plots (parallel-coordinate: memory-stall latencies, bar chart: % time in mem vs. compute) confirmed that, during the observed sub-second window, the force kernel was distinctly memory-bound (~70% of runtime in global-memory transfers). This analysis enables pinpointing root cause variability at fine temporal granularities and supports targeted optimization strategies.

6. Impact, Limitations, and Future Prospects

GPUscout bottleneck analysis enables reliable, scalable diagnosis of GPU kernel variability in High Performance Computing (HPC) and AI workloads. By fusing distributed processing, composable roofline metrics, statistical outlier detection, and direct topology determination (via MT4G), the workflow adapts to increasing trace complexities and rapidly changing hardware.

Significant improvements in automation and fidelity—such as the replacement of hard-coded parameters with live-measured values—minimize classification errors and drive actionable optimization (e.g., prefetching or cache tiling for memory-bound kernels, kernel fusion for compute-bound cases). However, ongoing research is addressing measurement gaps for certain hardware components and throughput benchmarking methods.

A plausible implication is that future bottleneck analysis frameworks may further integrate real-time benchmarking or topology discovery, narrowing latency between hardware deployment and actionable performance diagnostics. The GPUscout-MT4G methodology directly informs dynamic resource partitioning, hardware-aware kernel optimization, and next-generation automated HPC workflows.